The Relationships Among Adult Affective Factors,Engagement in Science,and Scientific Competencies

Abstract

This study investigated the relationship among adult affective factors, engagement in science, and scientific competencies. Probability proportional to size sampling was used to select 504 participants between the ages of 18 and 70 years. Data were collected through individual face-to-face interviews. The results of hierarchical regression analysis showed that while controlling demographic variable factors, science-related affective factors held explanatory power for scientific competencies. Among those factors, the explanatory power of self-efficacy was the greatest, followed by enjoyment and interest in science. While controlling antecedent factors, engagement in science held explanatory power for scientific competencies. It is suggested that improving affective factors and engagement in science may enhance adult scientific competencies. In terms of adult education, this study suggests that with more accessible science resources, adults may have the potential to trigger their interest, increase their self-confidence, and engage themselves in scientific issues.

Keywords

affective factors engagement in science scientific competencies scientific literacy self-efficacy

Introduction

One of the major goals of science education is to promote scientific literacy (Jack, Lin, & Yore, 2014; Woods-McConney, Oliver, McConney, Schibeci, & Maor, 2014), the research paradigms for which have shifted over time (Bauer, Allum, & Miller, 2007). Central to contemporary notions of scientific literacy is “the ability to engage with science-related issues, and with the ideas of science, as a reflective citizen” (Organisation for Economic Co-operation and Development [OECD], 2010, p. 128; OECD, 2013). Several scholars have suggested that the investigation of scientific literacy should extend from school education to adult and lifelong learning (Falk & Needham, 2013; Falk, Storksdieck, & Dierking, 2007; Sarkar & Corrigan, 2014) and that it should be both broad and applied (OECD, 2013). The importance of scientific literacy is relevant to scientific issues related to personal life or to important competencies required to effectively participate in related public discussions (Bauer et al., 2007; Lin, Hong, & Huang, 2012; Miller, 2004). The OECD (2010) had therefore suggested scientific literacy centers on scientific competencies which can be applied in the context of adult life. With this focus, a learner with more developed scientific literacy will demonstrate the ability to make wise decisions, give reasonable explanations for science-related phenomena in her or his daily life (Bybee, 2008), and undertake actions in political and public life based on critical reflection (Miller, 2004; OECD, 2010).

In recent years, some researchers have pointed out that affective factors also play an important role in the formal learning (Lin et al., 2012) or adult education (Cross, 2009; Jameson & Fusco, 2014). Affective factors could be positive or negative and they may have effects on learning (Shuck, Albornoz, & Winberg, 2007). The important affective factors for science learning include science-related interest, enjoyment, self-efficacy, and self-concept (Jack et al., 2014; Lin, Lawrenz, Lin, & Hong, 2013). Shepard, Fasko, and Osborne (1999) pointed out that individuals with high emotional intelligence may deal with affective factors related to physical health, job performance, and learning in a better way. Adult learners prefer self-directed learning and being placed in a vast educational environment, and they exhibit a readiness and a high degree of intrinsic motivation to learn (Boucouvalas & Lawrence, 2010; Brookfield, 1993; Cross, 2009; Eneau, 2008; Jameson & Fusco, 2014; Marsick & Watkins, 2001; Merriam, Caffarella, & Baumgartner, 2007; Stine-Morrow & Parisi, 2011). Adult science learning may come from engagement in science, which consists of accessing scientific information or visiting science-related venues to satisfy one’s curiosity, interests, or entertainment needs (Cross, 2009; Falk et al., 2007; Falk & Needham, 2013; Fenichel & Schweingruber, 2010; Marsick & Watkins, 2001).

The above discussion illustrates that different approaches may need to be considered in promoting adult scientific literacy (Falk & Needham, 2013). Much research has also indicated that adult learners are more strongly motivated by internal factors (Jameson & Fusco, 2014). However, few studies on scientific competencies thus far have considered the roles of affective factors and engagement in science during the formation of scientific competencies (Lin et al., 2012). Although some have criticized the OECD’s test in a minor way by expressing doubt as to whether it can accurately assess the full range of learners’ scientific competencies (Sjøberg, 2015). It is important for the adults to have these competencies, which enable them to properly engage in public affairs in democratic society. Science educators are worthy of contributing to this domain by providing policy suggestions to the government. Therefore, this study aimed to investigate the relationship among adult affective factors, engagement in science, and scientific competencies.

The Testing Framework for Adult Scientific Competencies in Taiwan

The OECD established a definition and a testing framework for scientific literacy that was applied to the Programme for International Student Assessment (PISA). The definition of scientific literacy is “the capacity to use scientific knowledge, to identify questions and to draw evidence-based conclusions in order to understand and help make decisions about the natural world and the changes made to it through human activity” (OECD, 2010, p. 128). In this framework, science literacy denotes an overarching scientific competency which is defined as “the capacity to mobilise cognitive and non-cognitive resources in any given context” (OECD, 2010, p. 126). In order to guide research into adult scientific literacy in Taiwan, Lin (2010) and Taiwan Ministry of Science and Technology referenced existing research on scientific literacy, including PISA testing framework, to develop a research framework.

During the development of the assessment framework, Lin (2010) consulted past research to grasp the current evaluation and survey methods used in and outside of Taiwan, in addition to holding multiple expert panels to collect varying opinions. Propositional planning for this assessment framework used adult daily life and various situations to package scientific and technological questions. This testing framework could then be used to develop survey tools for the investigation of adult scientific competencies (Figure 1). These competencies include individual interests, values, and actions related to scientific events (OECD, 2010) and transverses context, knowledge, and attitudes.

Figure 1.

Testing framework for public scientific competencies in Taiwan (Lin, 2010).

Specifically, the Competencies dimension, which is the core in Figure 1, comprises the following four constructs (OECD, 2010; Lin, 2010): (a) identifying scientific issues, (b) explaining phenomena scientifically, (c) using scientific evidence, and (d) solving problems technically. The assessment framework in Taiwan differs from PISA in that it adds a fourth construct—solving problems technically. As Lin (2010) organized the assessment framework, experts suggested that scientific literacy should consider science education standards in Taiwan and include problem-solving skills. In other words, they suggested that adult ability to process events and solve problems during experimentation or observation should also be considered.

As shown in Figure 1, the Context dimension is adopted from the PISA framework (OECD, 2010) and is assessed using life situations including health, natural resources, environment, hazard, and frontiers of science and technology; each situation intertwines the personal, social, and global scales. The Attitudes dimension includes interests, support for scientific inquiry, and the responsibilities of a technological society. The Knowledge dimension includes knowledge of science and knowledge about science. The former consists of the understanding of scientific truths, while the latter consists of the understanding of scientific processes. This testing framework was not constrained by declarative knowledge but required the use of procedural knowledge, which is the knowledge utilized in the task of problem solving.

Bauer et al. (2007) argued that the previous scientific literacy studies heavily focused on static declarative knowledge. PISA studies have provided new insights into the interconnections between measures of knowledge, affect, and value as scientific competencies (Jack et al., 2014). Bauer et al. (2007) also pointed out that current studies in scientific literacy have already shifted from traditional research paradigm to individuals’ attitudes toward scientific issues and put more emphasis on the relationship between science and society. Research frameworks such as PISA can expand the research agenda (Bauer et al., 2007). This study thus adopted the framework in Figure 1 proposed by Lin (2010) for the survey of adult scientific competencies.

Scientific competencies emphasize the technical skills citizens need for their adult lives and the basic ability to adapt to modern life (OECD, 2010). For example, the prevalence of dengue fever is currently a serious issue in Southern Taiwan. Adults with more developed scientific competencies should have the ability to make judgments on which methods are suitable to prevent the distribution of dengue fever. Therefore, this study mainly measured these basic competencies. The participants in this survey were required to read short stories, magazine articles, and statistical figures and then answer questions that encompass each scientific competency.

Affective Factors and Engagement in Science

Several researchers have pointed out that affective factors motivate and are related to science learning achievement or scientific literacy (Bauer et al., 2007; Jansen, Scherer, & Schroeders, 2015; Lin et al., 2012; Pantziara & Philippou, 2015; Wlodkowski, 2003; Woods-McConney et al., 2014) or adult education (Boucouvalas & Lawrence, 2010; Cross, 2009; Jameson & Fusco, 2014). Affective factors can be explored as being either positive or negative (Shuck et al., 2007). For example, Jameson and Fusco (2014) pointed out that negative self-perceptions may hinder adults’ learning. In their studies regarding affective factors and learners’ scientific competencies, Lin et al. (2013) and Jack et al. (2014) concluded that affective factors include science-related interest, enjoyment, self-efficacy, and self-concept.

Interest is often defined as an individual’s psychological state that favors a certain field, event, or idea that is developed through interactions with the environment (Krapp, 2005). Scholars have pointed out that learning interest can motivate learners to learn and inspire learners to put more effort into learning (Fenichel & Schweingruber, 2010). Enjoyment consists of positive emotions in response to learning activities. Such emotions can produce meaningful awareness in the related field (Jack et al., 2014). Self-efficacy refers to self-confidence in one’s ability to complete a task (Bandura, 1977; Stine-Morrow & Parisi, 2011). This judgment of self-confidence influences an individual’s choice of actions, level of effort, and amount of time allocated. Compared with young people, adults have been found to have reduced levels of self-efficacy (Jameson & Fusco, 2014; Stine-Morrow & Parisi, 2011). Self-concept, meanwhile, refers to the perception of one’s own experiences in academics or society (Jack et al., 2014; Marsh, Trautwein, Ludtke, Koller, & Baumert, 2005). Learners’ positive experiences with learning science positively affect their self-concepts (Jack et al., 2014; Porras-Hernández & Salinas-Amescua, 2012) and are related to those learners’ self-concepts and learning achievements (Chang, Singh, & Mo, 2007). Self-efficacy and self-concept are often referred to as self-related cognitions (Jack et al., 2014). The difference is that self-efficacy refers to the future-oriented confidence one has regarding one’s expectations to accomplish a given task or an approaching test, while self-concept refers to the past-oriented confidence one has due to past experiences in learning science (Lin et al., 2013; Porras-Hernández & Salinas-Amescua, 2012).

The subjective negative affective factors are called affective filters and may have a different impact on adult learning (Shuck et al., 2007). In science communication, negative affective responses may cause adults to exhibit anxiety, mistrust of science, or beliefs that governmental or scientific organizations are inclined toward economic gain (Priest, 2001). Once adults are aware of the uncertain risk of science products, their interest in such products may increase and makes them obtain more information about the products (Van de Velde, Verbeke, Popp, & Huylenbroeck, 2011), and social movements may take shape (Hill, 2008). This awareness may either lead adults to engage in science or to avoid science-related products.

Engagement in science refers to the extent of participation in scientific learning activities (Billett, 2002; Chang et al., 2007). For adults, it consists of participation in scientific activities during leisure time, including reading or watching science-related television programs, books, websites, radio broadcasts, magazines, or newspapers (Bonney, Phillips, Ballard, & Enck, 2016; Falk et al., 2007; OECD, 2006a; Woods-McConney et al., 2014). Sandlin, Wright, and Clark (2013) pointed out that public pedagogy is a concept which refers to the influence of media on adult learning. Adult learning may occur via the public pedagogy of popular culture (i.e., through television, the Internet, and magazines; Sandlin et al., 2013; Tisdell, 2003). Adult science learning rarely occurs in schools; rather, various sources of informal education are more abundant and accessible (Falk et al., 2007; Fenichel & Schweingruber, 2010; Marsick & Watkins, 2001). Engagement with public pedagogy may also foster adult transformational learning, which refers to the processes through which adults reflect on their own beliefs or knowledge and explore new knowledge (Sandlin et al., 2013). This process of engagement may help adults clarify their positions on various social issues through critical thinking.

Adult Affective Factors, Engagement in Science, and Scientific Competencies

It is crucial to investigate the scientific literacy that correlates with the interests and activities of adults outside the scientific community (Kawamoto, Nakayama, & Saijo, 2011). Some researchers have described adult learning as the process of making sense of experiences (Boucouvalas & Lawrence, 2010; Hill, 2008; Marsick & Watkins, 2001; Merriam et al., 2007). This kind of learning can be viewed as a quest for authenticity (Hill, 2008) and consists of both selective and self-directed learning (Brookfield, 1993; Cross, 2009; Eneau, 2008; Jameson & Fusco, 2014; Marsick & Watkins, 2001; Stine-Morrow & Parisi, 2011). During this self-directed learning process, an individual’s own sense of autonomy directs the individual’s learning and takes into account the relevant affective factors (Eneau, 2008; Roberson & Merriam, 2005). In the context of adult education, the engagement of individual adults with culture, media, and leisure during adulthood may have effects on their competencies or literacy (Falk et al., 2007; Stine-Morrow & Parisi, 2011; Tett & St. Clair, 2011).

Lin et al. (2013) used the PISA database to investigate affective factors and engagement in science to predict the PISA scientific competencies of 15-year-old students. The results formulated a predictive model that explains learners’ possible future interest in science. Wlodkowski (2003) proposed a motivational framework for adult learning which includes establishing inclusion, developing attitude, enhancing meaning, and engendering competencies. Falk et al. (2007) investigated the relationship between adult interest in science and adult understanding of science and found that science understanding might be acquired for reasons related to personal interest. Falk and Needham (2013) surveyed 1,018 people in Los Angeles and found that predictive factors for adult self-reported knowledge included engagement in science, formal schooling, and work experience. Within Taiwan, Wu et al. (2012) sampled 2,024 adults to investigate their attitudes toward scientific issues. Within their model, a latent variable which comprised adult interest and engagement in science construct was highly correlated to scientific literacy.

Lin et al. (2013) and Wlodkowski (2003) provided insight into the roles that affective factors and engagement play in adult competencies. However, Lin et al.’s (2013) study was conducted using students who were only 15 years, and Wlodkowski (2003) did not provide empirical data to support the proposed framework. This illustrates that surveys conducted with adult groups still hold importance. Although Falk et al. (2007) and Wu et al. (2012) used adult groups as their research subjects, the studies of Lin et al. (2013) and Wlodkowski (2003) proposed a more comprehensive model by including affective factors. The above reviews indicate that a study that establishes a distinguishing model to assess the predictive effects of affective factors and engagement in science in terms of scientific literacy is still lacking.

Research Questions

This study used actual adult groups to investigate the roles that affective factors and engagement in science play in relation to adult scientific competencies. The research questions were as follows:

Research Question 1: To what extent can adult scientific competencies be predicted by affective factors?

Research Question 2: To what extent can adult scientific competencies be predicted by engagement in science?

Method

Data Collection

The subject population in this study was composed of Taiwanese adults between the ages of 18 and 70 years. The cluster analysis was used to divide the townships/district in Taiwan into six stratums (Table 1) using six urbanization indicators (population density, percentage of the population aged 15 to 64 years, percentage of the population more than age 65, educational level, percentage of the industrial population, and percentage of the service population) proposed by Wu et al. (2012) and Shein, Li, and Huang (2015). Three phases of probability proportional to size sampling were conducted for each stratum according to “township/district—village—citizen” and samples were selected by systematic sampling in accordance with population proportion (Table 1). After selecting 60 villages to predict the failure interviews and guarantee that the target interview could be achieved, citizens samples in each village were expanded by the (success + failure)/success ratios which were recommended by previous study (Wu et al., 2012). The expanded number of participants was 1,554 and the final successful sample was 504 citizens. Within the successful sample, 52.6% were male and 47.4% were female; 26.4% were between the ages of 18 and 29 years, 23.4% were between 30 and 39 years, 16.9% were between 40 and 49 years, 20.2% were between 50 and 59 years, and 13.1% were between 60 and 70 years.

Table 1.

The Samples for Each Stratum and Phase.

Stratum	No. of townships/districts	Population	Proportion	Phase 1: Sampled townships/districts	Phase 2: Sampled villages	Phase 3: Sampled citizen	Expanded citizen samples	Completed samples
1	25	3,564,655	0.221	6	12	130	386	93
2	40	4,387,129	0.272	8	16	160	466	140
3	73	4,480,267	0.277	8	16	170	417	122
4	47	1,334,942	0.083	2	4	48	96	43
5	98	1,718,978	0.106	4	8	66	137	66
6	75	667,761	0.041	2	4	26	52	40
Total	358	16,153,732	1.000	30	60	600	1,554	504

Instruments

The dependent variable was the construct of adult scientific competencies. The adult scientific competencies assessment (Table 2) was designed according to the testing frameworks detailed in Figure 1 and the Context dimension in PISA framework (OECD, 2010, p. 131), which are discussed in the Introduction section. The assessment was composed of five groups with a total of 11 items. A full score was 11 points. The contexts within these items revolved around important scientific topics in Taiwan. The items were tested by 100 people in a pilot study conducted with a convenience sample to ensure that the wording of items could be understood by the participants. Five science educators also reviewed the items to achieve expert validity. In the assessment, there were five contexts spanning four competency indicators; items were either multiple choice or true or false. They were compiled into the two-way specification table shown in Table 2 to achieve content validity. In terms of construct validity, Rasch (1960) model analysis revealed that the mean square error for goodness of fit was between 0.94 and 1.05 which is within the acceptable cutoff range of 0.6 to 1.4 (Linacre & Wright, 1994). T values were between −1.8 and 1.0, which is within the acceptable cutoff range of −1.96 to 1.96. Separation reliability was 1.00, which was above the ideal cutoff of 0.9 (Waugh & Addison, 1998). All of above indicate acceptable reliability and validity. Estimated difficulty was between −2.23 and 1.84; lower estimated values indicate the items were easier to answer correctly. Examples of the items are provided at online (http://iris.ge.nsysu.edu.tw/csl/).

Table 2.

The Two-Way Specification Table for the Adult Scientific Competencies Assessment.

		Competency					Goodness of fit
Situation	Item code	A	B	C	D	Estimated difficulty	MNSQ	T	Type of item
Dengue fever (health)	9.1		○			−2.23	1.03	0.3	Multiple choice
Dengue fever (health)	9.2	○				−0.03	0.99	−0.4	Multiple choice
Water resources (natural resources)	10.1			○		−0.63	1.01	0.2	Multiple choice
Water resources (natural resources)	10.2		○			−0.34	0.98	−0.5	Multiple choice
Environmental quality (environment)	11.1	○				−0.10	0.94	−1.8	True or false
	11.2		○			1.18	0.98	−0.3	True or false
	11.4			○		−0.93	1.01	0.2	True or false
Flooding (hazard)	12.1				○	1.84	1.03	0.4	True or false
Flooding (hazard)	12.2	○				−0.03	1.05	1.2	Multiple choice
Earthquake-resistant construction (frontiers of science and technology)	13.1				○	0.39	1.03	1.0	True or false
	13.2				○	1.55	0.97	−0.4	Multiple choice

Note. MNSQ = mean square error; A = identifying scientific issues; B = explaining phenomena scientifically; C = using scientific evidence; D = solving problem technically. Separation reliability = 1.00.

The independent variables were the affective factors and engagement in science. The questionnaire, which adopted a 4-point Likert-type scale, is shown in Table 3. The questionnaire included the following science-related components: (a) Interest: the adult interest in science, such as the use of new inventions and technologies, new medical discoveries, and space exploration. (b) Enjoyment: the level of enjoyment when participating in science-related activities. (c) Self-efficacy: self-confidence in future science-related topics. (d) Self-concept: confidence in past science-related topics. (e) Engagement: individual participation in scientific activities.

Table 3.

The Questionnaire of Affective Factors and Engagement in Science.

Scale	Source	No. of items	Score range	Alpha	Example
Interest	NSB (2006)	5	5-20	.78	How interested are you in current news events? Issues about new scientific discoveries.
Enjoyment	OECD (2006b)	5	5-20	.91	How much do you agree with the statements below? I generally have fun when I am learning broad science topics.
Self-efficacy	OECD (2006b)	7	7-28	.86	How easy do you think it would be for you to perform the following tasks on your own? Identify the science question associated with the disposal of garbage.
Self-concept	OECD (2006b)	5	5-20	.90	How much do you agree with the statements below? I can easily understand new ideas in school science.
Engagement	OECD (2006b)	5	5-20	.75	How often do you do these things? Watch TV programmes about broad science.

Note. NSB = National Science Board; OECD = Organisation for Economic Co-operation and Development.

Research Processes

A total of 60 villages were sampled and 29 interviewers were trained at a training workshop. During the field research, each interviewer was responsible for one to three villages. Interviewers followed the names and addresses on the sample list and conducted face-to-face interviews. During the interview, the interviewer read each item aloud to the interviewee and the individual interviewee completed the survey by saying the answers to interviewer. The interviewer recorded the interviewee’s answers to prevent any influence from illiteracy or reading inability. Cue cards were also used to aid interviewees in understanding longer or more complex items and offering answer options. Interviewers maintained contact with the research team throughout the interview process to relay and discuss any problems encountered during the survey.

Data Analyses

During the analysis of research questions, after the data for the successful sample were weighted, they were analyzed using hierarchical regression. As hierarchical regression is a confirmation technique, variable sequence was founded on the theoretical foundation and whether independent variables are influenced by other independent variables (Chiu, 2010). This study was formulated in accordance with past literature; as such, the sequence of independent variables used was demographics block (including age and gender), affective factors block (including interest, enjoyment, self-efficacy, and self-concept), and engagement in science block (including engagement in science) to understand the predictive effects of these independent variables on dependent variable of adult scientific competencies.

Results

Descriptive Statistics

The means, standard deviations, skewness, and kurtosis for all observed variables are shown in Table 4. Mean values for observed variables were between 0.47 and 43.59. Standard deviations were between 0.49 and 14.63. Skewness were between −0.65 and 0.51. Kurtosis were between −1.99 and 1.27. A skewness below 3 and a kurtosis below 10 indicated observed variable data mostly conforms to normal distribution (Kline, 2005).

Table 4.

Descriptive Statistics and Pearson’s Correlation Matrix of the Variables.

Variables	1	2	3	4	5	6	7	8
M	0.47	43.59	13.34	13.77	17.15	12.72	5.08	4.94
SD	0.49	14.63	2.63	2.82	4.20	3.04	4.02	2.33
Skewness	0.13	−0.03	−0.48	−0.65	−0.51	−0.56	0.51	0.07
Kurtosis	−1.99	−1.12	1.27	1.22	−0.34	−0.65	−0.50	−0.56
1. Gender	1	−.09*	.15**	.24***	.15***	.24***	.27***	.19***
2. Age		1	−.19***	−.22***	−.20***	−.17***	−.19***	−.34***
3. Interest			1	.59***	.50***	.44***	.50***	.39***
4. Enjoyment				1	.52***	.61***	.57***	.43***
5. Self-efficacy					1	.64***	.40***	.43***
6. Self-concept						1	.46***	.38***
7. Engagement							1	.43***
8. Scientific competencies								1

p < .05. **p < .001. ***p < .001.

To test the correlations between observed variables, Pearson product-moment correlation was used to establish a correlation matrix (Table 4). The correlation coefficients for the observed variables were between −0.34 and 0.64 (p < .05). In addition, the dependent variable scientific competency was correlated with gender (r = .19), age (r = −.34), interest (r = .39), enjoyment (r = .43), self-efficacy (r = .43), self-concept (r = .38), and engagement (r = .43). The correlation coefficients for each independent variable were below 0.80, indicating the absence of multicollinearity (Kline, 2005). The value of the Durbin–Watson test for independence of errors was 1.47, which was within the acceptable range of values (Montgomery, Peck, & Vining, 2001). In the homoscedasticity test, the scatter plot showed a symmetrical form. The above discussion shows that the data met the assumptions of linear regression.

The Effects of Demographics

Table 5 shows that the explanatory power of demographics variables on dependent variables reached significance (R² = .145, F = 42.337, p < .001); the two independent variables could explain 14.5% of the variations in dependent variables. The individual predictive effect (β) of the gender variable was .164 (t = 3.954, p < .001); the mean score for men was greater than that for women. The individual predictive effect (β) of the age variable was −.327 (t = −7.874, p < .001); the mean score of younger adults was higher.

Table 5.

The Hierarchical Regression Analysis for Adult Scientific Competencies.

	Block 1		Block 2		Block 3
Variables	β	t	β	t	β	t
Gender	.164	3.954***	.073	1.893	.050	1.290
Age	−.327	−7.874***	−.228	−5.918***	−.220	−5.801***
Interest			.118	2.446*	.074	1.523
Enjoyment			.163	3.042**	.101	1.837
Self-efficacy			.197	3.839***	.195	3.847***
Self-concept			.047	0.877	.024	0.451
Engagement					.194	4.096***
Multiple R		.380		.560		.579
R ²		.145		.313		.336
F		42.337***		37.745***		35.777***
ΔR²		.145		.168		.023
F change		42.337***		30.468***		16.777***

p < .05. **p < .01. ***p < .001.

The Effects of Affective Factors

Table 5 shows that the explanatory power of affective factors on dependent variables reached significance (R² = .313, F = 37.745, p < .001). The significance of explanatory power for this block (ΔR² = .168, F = 30.468, p < .001) indicates that inclusion of affective factors can effectively increase the explanatory power of the model. While controlling demographic variable factors, affective factors contributed an additional 16.8% explanatory power. Among the four independent variables, self-efficacy had the greatest contribution (β = .197, t = 3.839, p < .001). This indicates that when people had higher confidence in future science-related undertakings, their scientific competencies also were higher. Contributions from enjoyment (β = .163, t = 3.042, p < .001) and interest (β = .118, t = 2.446, p < .001) were the next greatest, indicating that people who were greater enjoyment in science-related activities and who were more interested in scientific topics had higher scientific competencies. Self-concept had no statistical meaning in this model.

The Effects of Engagement in Science

Table 5 shows that the explanatory power of engagement in science on dependent variables reached significance (R² = .336, F = 35.777, p < .001). The significance of explanatory power for this block (ΔR² = .023, F = 16.777, p < .001) indicates that inclusion of engagement in science can effectively increase the explanatory power of the model, increasing the overall explanation of variations in scientific competencies to 33.6%. While controlling demographic variables and affective factors, engagement in science provided an additional 2.3% explanatory power. This indicates that people who participated more in activities that provide science information had higher scientific competencies.

Discussion

The Effects of Affective Factors

The results showed that while controlling demographic variable factors, affective factors held explanatory power for scientific competencies. In this model, predictive effect for self-efficacy was the greatest, followed by enjoyment and interest. The model explanation of variations for adult scientific competencies reached 31.3% (R² = .313). This result was consistent with the findings in Falk et al.’s (2007) study. Falk et al. (2007) investigated the relationship between adult interest and adult understanding of science in two citizen groups and found that adult understanding of science and adult interest were mutually correlated. Related research indicated that interest (Fenichel & Schweingruber, 2010; Wlodkowski, 2003) and self-efficacy (Jameson & Fusco, 2014), which may help motivate adult cognitive functions and perseverance, are influencing factors for adult science and math learning. This study provided additional insight and indicated that these affective factors may also play an important role in promoting adult scientific competencies. Science educators and policy makers are reminded that extra efforts might be needed in triggering learners’ interest and self-efficacy for these prospective young adults.

The results of the study also showed that among self-related cognition, future-oriented self-efficacy had a higher predictive effect for adult scientific competencies than past-oriented self-concept. Self-efficacy plays an important role in learning and may be particularly crucial in learning during adulthood (Stine-Morrow & Parisi, 2011). Adults face various challenges in life. The confidence gained from the past science learning in schools cannot provide the self-confidence required to face future science-related issues such as environmental conservation and unprecedented natural disasters and diseases. Moreover, the self-confidence to handle future scientific issues may stem from motivation. Adults with stronger motivation are more willing to understand these scientific issues and may have the necessary scientific competencies to confront them.

The Effects of Engagement in Science

This study found that while controlling demographic variable and affective factors, engagement in science held explanatory power for scientific competencies. The explanation of variations for adult scientific competencies reached 33.6% (R² = .336). This result was consistent with the findings in Tsai et al. (2013) and Falk and Needham (2013). Tsai et al. (2013) found that engagement in science-related media and venues can predict adult scientific competencies. Falk and Needham (2013) found that the use of media to acquire scientific information was included in factors to predict self-reported scientific knowledge. Engagement in learning is the most conspicuous outcome of motivation (Wlodkowski, 2003). Adults who persist with self-directed learning and transformational learning may regularly engage in informal information exchanges and dialogues with their peers to reflect on the learning process, and have a higher chance of improving their scientific competencies.

Lin et al. (2013) found no predictive effect of engagement in science for scientific competencies in the adolescent cohort. However, this study found that with regard to adult participants, engagement in science was an important predictor variable for scientific competencies. Fifteen-year-old Taiwanese adolescents are typically preparing to transition into senior high schools; the formation of scientific competencies may not be dominated by information from informal education. However, adult learning is grounded in everyday life experiences and informal resources (Fenichel & Schweingruber, 2010; Marsick & Watkins, 2001; Sandlin et al., 2013). Relative to childhood learning experiences, learning experiences in adulthood are more varied and closer to leisure activities (Stine-Morrow & Parisi, 2011), which often include involvement with media and popular culture (Sandlin et al., 2013). When they are engaged in learning, adult learners are active and may effectively be searching for, evaluating, or organizing various kinds of learning materials to form new ideas or achieve new understanding (Wlodkowski, 2003). The testing contexts for adult scientific competencies in this study accurately reflected life, and adults with higher engagement in science may more often acquire this scientific understanding from media.

Adults may construct their meaning system through these media of public pedagogy, and transformational learning occurs through these experiences (Sandlin et al., 2013). However, Stilgoe and Lock (2014) argued that scholars considering the concept of engagement in science should consider the context of adults living in democratic societies. They pointed out that adults may be antiscientific and that engagement in science is interactive, such that the effects of expert reassurance should be considered. In addition, this study also found that certain effects (e.g., interest and enjoyment) disappeared from Block 3 with the introduction of engagement. One explanation for this might be that engagement in science covered the explanation of variations of interest and enjoyment. Engagement in science is a complex factor, while science itself is seen as difficult to understand. Adults who engage more in science may have higher interest in and greater enjoyment of science.

The Effects of Demographics

This study found that demographic variables held explanatory power for scientific competencies. Formal education or adult learning environments have improved the scientific competencies of younger generations but have not yet ameliorated the gender differences found for various competencies. Moreover, this study also found that gender had an effect in Block 1 but showed no effect in Blocks 2 and 3. This may be due to the fact that affective factors, engagement in science, and the effects of gender had some overlaps in their explanatory powers for scientific competencies. Gender differences were also found in adult affective factors and engagement in science.

The Limitations of this Study

There are various definitions of and survey methods for measuring scientific literacy (Kawamoto et al., 2011). In addition, the research paradigm of scientific literacy has shifted over time (Bauer et al., 2007). This study adopted the framework proposed by Lin (2010) and Wlodkowski (2003) and investigated adult scientific competencies as the representation of scientific literacy. Nevertheless, scientifically literate adults might not express their abilities well in such testing frameworks (Sjøberg, 2015). Future researchers may replicate the same research questions and verify the results by using another approach.

Conclusions

This study investigated the scientific competencies that are crucial for allowing adults to have reasonable opinions about and to effectively participate in current and future socioscientific issues (OECD, 2010), such as the environmental movement. The scientific competencies enable adults to produce reasonable explanations of and make wise decisions regarding these issues (Bybee, 2008; OECD, 2010). A survey and model to predict adult scientific competencies were established and strict procedures were used to complete this study. The interviews found that affective factors and engagement in science could predict adult scientific competencies. While controlling demographic variable factors, affective factors held explanatory power for scientific competencies; among these affective factors, self-efficacy had the greatest predictive power, followed by enjoyment and interest. Self-concept had no predictive power. While controlling demographic variables and affective factors, engagement in science held explanatory power for scientific competencies.

The results from this study that might reflect Wlodkowski’s (2003) motivational framework in certain respects indicate that improving adults’ self-confidence and increasing their enjoyment and interest in scientific activities and in learning scientific information may help raise their scientific competencies. Jameson and Fusco (2014) found that adult learners exhibit less self-confidence about their competencies because they perceive themselves as less competent than those currently learning in an academic environment. As social networks have come to play an important role in citizens’ communications, interns who stay in touch with each other through science-related social networks could share interests (Fenichel & Schweingruber, 2010) and feedback with one another, sharing which can be helpful for the development of self-efficacy (Jameson & Fusco, 2014; Jansen et al., 2015). In school education, in addition to nurturing future scientists or technicians (Kawamoto et al., 2011), teaching young adults to be self-confidence and interested in life sciences is also important.

This study illustrates that increasing the exposure to scientific information may improve adults’ scientific competencies, including contact with science-related media. The challenge in media communication is to make science experiences come alive (Bauer et al., 2007; Falk et al., 2007), and to teach learners through interactivity, such as telling a story about scientific inquiry (Fenichel & Schweingruber, 2010). Adults are often motivated by the science-related events or information relevant to their lives (Fenichel & Schweingruber, 2010). Marsick and Watkins (2001) suggested that adult educators may help learners identify scientifically relevant conditions in the sociocultural context. This process may engage adult learners with the concepts relevant to science process skills and enhance their ability to draw conclusions about social issues with evidence-based reasoning (OECD, 2006a).

In general, adult learners are experiencing negative self-perceptions and affect that may hinder their learning (Jameson & Fusco, 2014). In terms of adult education, this study suggests that with more accessible science resources (Bonney et al., 2016; Marsick & Watkins, 2001) may have the potential to trigger their interest, increase their self-confidence, and engage themselves in scientific issues. The scientific process skills spread by technology-facilitated interactions may be learned incidentally by adult learners (Marsick & Watkins, 2001). The popularization of the Internet (Fenichel & Schweingruber, 2010; Hill, 2008) and mobile devices may help in distributing interactive and interesting short films, animations, and comics for the purpose of self-directed and transformational learning (Cranton, 2006; Fenichel & Schweingruber, 2010). In this study, self-efficacy and engagement in science were found to be two crucial factors for adult scientific competencies. In future research, it is suggested that the factors affecting adult self-efficacy and engagement in science should be explored further.

Footnotes

Acknowledgements

The authors greatly appreciate the assistance of Prof. Huann-shyang Lin and Prof. Tai-Chu Huang and her research team. The authors also would like to thank the journal reviewers and editors for their helpful comments on earlier drafts of this manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The work reported here was supported by the Ministry of Science and Technology, Taiwan, under grant NSC 101-2511-S-110-007-MY3.

Author Biographies

Chun-Yen Tsai is an assistant professor at National Sun Yat-sen University. His research focuses on educational technology and science education. Currently, he participates in some projects granted by the Ministry of Science and Technology in Taiwan, which are related to the studies about educational technology and adult science education.

Yuh-Yuh Li is an assistant researcher at National Sun Yat-sen University. His research interests are about public understanding of science, scientific culture, public engagement in science, science centers, paranormal behavior, and social disorganization theory. He is also the co-editor of several reports on the Adult Scientific Literacy Survey in Taiwan.

Ying-Yao Cheng is a professor of National Sun Yat-sen University. Currently, he is the president of National Sun Yat-sen University. His research focuses on educational psychology and administration. He participates in several important projects granted by the Ministry of Science and Technology in Taiwan, including the Adult Scientific Literacy Survey.

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